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Activity Number: 436
Type: Contributed
Date/Time: Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #305035
Title: Outlier Detection in Functional Data Analysis
Author(s): Pallavi Sawant*+ and Nedret Billor
Companies: Auburn University and Auburn University
Address: Dept. of Mathematics and Statistics, Auburn, AL, 36849,
Keywords: Functional data ; Functional principal components ; Functional linear model ; Influential observations ; Robustness
Abstract:

Exploratory methods in functional analysis are outlier sensitive. This paper proposes a new tool for identifying functional outliers by using robust functional principal components in a functional data. We also show that the diagnostic measures for identifying influential observations in a functional regression model, where regressor is functional and response is scalar, can be defined by using the proposed robust functional principal components to overcome the problem of infinite dimension of the regressor. Robustness of the proposed procedure is studied by simulation and several benchmark data sets.

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